In order to further improve the precision and generalization ability of the neural network based performance model of engine, back propagation neural network (BPNN), radial basis function neural network (RBFNN) and generalized regression neural network (GRNN) have been investigated. The topologies and algorithms of these three different types of neural networks have been designed to meet the sa...

An artificial neural network is developed to recognize spatio-temporal bipolar patterns associatively. The function of a formal neuron is generalized by replacing multiplication with convolution, weights with transfer functions, and thresholding with nonlinear transform following adaptation. The Hebbian learning rule and the delta learning rule are generalized accordingly, resulting in the lear...

Cephalometric analysis of lateral radiographs of the head is an important diagnosis tool in orthodontics. Based on physically locating specific landmarks, it is a boring, lengthy and error prone task. The objective of this work is to calculate the SNA angle, SNB angle and ANB angle between the landmarks to identify the input and output parameters pertaining to skeletal abnormalities. By doing s...

the method of artificial neural network is used as a suitable tool for intelligent interpretation of gravity data in this paper. we have designed a hopfield neural network to estimate the gravity source depth. the designed network was tested by both synthetic and real data. as real data, this artificial neural network was used to estimate the depth of a qanat (an underground channel) located at...

the aim followed in this study was to compare the performance of multiple regression vs neural network models to predict the activity of antioxidant enzymes super oxide dismutase (sod), cat alase (cat), ascorbate pero xidase (apx) and peroxidase (pox) in the shoots of wheat (triticum aestivum), alvand cultivar in a soil polluted with cadmium. the treatments consisted of four levels of cadmium (...

This paper proposes a neural network approach to efficiently solve nonlinear convex programs with the second-order cone constraints. The neural network model is designed by the generalized Fischer–Burmeister function associated with second-order cone. We study the existence and convergence of the trajectory for the considered neural network. Moreover, we also show stability properties for the c...

In this paper, We interpret a fuzzy differential equation by using the strongly generalized differentiability concept. Utilizing the Generalized characterization Theorem. Then a novel hybrid method based on learning algorithm of fuzzy neural network for the solution of differential equation with fuzzy initial value is presented. Here neural network is considered as a part of large field called ...